On your Eta Compute ECM3532

Impulses can be deployed as a C++ library. This packages all your signal processing blocks, configuration and learning blocks up into a single package. You can include this package in your own application to run the impulse locally. In this tutorial you'll export an impulse, and build an application for the Eta Compute ECM3532 AI Sensor or Eta Compute ECM3532 AI Vision to classify sensor data.

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Knowledge required

This tutorial assumes that you're familiar with building applications for the ECM3532 AI Sensor or AI Vision, and have your environment set up to compile applications for this platform. If you're unfamiliar with these tools you can build binaries directly for your development board from the Deployment page in the studio.

Note: Are you looking for an example that has all sensors included? The Edge Impulse firmware for the Eta Compute ECM3532 has that. See edgeimpulse/firmware-eta-compute-ecm3532.

Prerequisites

Make sure you followed the Continuous motion recognition tutorial, and have a trained impulse. Also install the following software:

You will also need a way to flash your device. Either:

  • A SparkFun FTDI Breakout board and the Edge Impulse CLI (instructions).
  • Or, a SEGGER J-LINK Debug probe.

Cloning the base repository

We created an example repository which contains a small application, which takes the raw features as an argument, and prints out the final classification. You can either download the application or import this repository using Git:

$ git clone https://github.com/edgeimpulse/example-standalone-inferencing-ecm3532

Deploying your impulse

Head over to your Edge Impulse project, and go to Deployment. From here you can create the full library which contains the impulse and all external required libraries. Select C++ library and click Build to create the library.

Download the .zip file and extract the directories in the example-standalone-inferencing-ecm3532/Thirdparty/edge_impulse folder. Make sure to not replace CMakeLists.txt in this folder.

Running the impulse

With the project ready it's time to verify that the application works. Head back to the studio and click on Live classification. Then load a validation sample, and click on a row under 'Detailed result'.

Selecting the row with timestamp '320' under 'Detailed result'.Selecting the row with timestamp '320' under 'Detailed result'.

Selecting the row with timestamp '320' under 'Detailed result'.

To verify that the local application classifies the same, we need the raw features for this timestamp. To do so click on the 'Copy to clipboard' button next to 'Raw features'. This will copy the raw values from this validation file, before any signal processing or inferencing happened.

Copying the raw featuresCopying the raw features

Copying the raw features

In the example directory open src/main.cpp and paste the raw features inside the static const float features[] definition, for example:

static const float features[] = {
    -19.8800, -0.6900, 8.2300, -17.6600, -1.1300, 5.9700, ...
};

Then build the application by opening a terminal or command prompt, navigating to the 'example-standalone-inferencing-ecm3532' folder and run:

AI Sensor

$ cd Applications/edge-impulse-standalone
$ mkdir build
$ cd build
$ cmake ..
$ make loadconfig CONFIG="../ai-sensor-boot-config"
$ make -j

AI Vision

$ cd Applications/edge-impulse-standalone
$ mkdir build
$ cd build
$ cmake ..
$ make loadconfig CONFIG="../ai-vision-boot-config"
$ make -j

This generates binary files in the build/ directory that can be flashed on the development boards using the FTDI breakout board. If you want to flash using a JLINK, see the GitHub repo.

To flash the binary, run:

$ make flash_bl

Seeing the output

To see the output of the impulse, connect to the development board over a serial port (instructions for the Eta Compute ECM3532 AI Sensor) on baud rate 115,200. You can do this with your favourite serial monitor or with the Edge Impulse CLI:

$ edge-impulse-run-impulse --raw

This will run the signal processing pipeline, and then classify the output:

Edge Impulse standalone inferencing (ECM3532)
Running neural network...
Predictions (time: 2 ms.):
idle:   0.015319
snake:  0.000444
updown: 0.006182
wave:   0.978056
Anomaly score (time: 1 ms.): 0.133557
run_classifier_returned: 0
[0.01532, 0.00044, 0.00618, 0.97806, 0.134]

Which matches the values we just saw in the studio. You now have your impulse running on your ECM3532 development board!


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